Welcome to the IoT Slam X Virtual, Internet of Things Conference - December 11th, held on-line in the cloud

Bringing AI to the Edge – Are you ready for it?

Juthika Khargharia, Ph.D, Global IoT Evangelist, SAS
20 Jun 2019
15:35 - 16:10
Keynote Hall
Bringing AI to the Edge – Are you ready for it?

Bringing AI to the Edge – Are you ready for it?

Session Abstract:

SASThe internet of things presents exciting opportunities to apply artificial intelligence for transformative business growth. Advanced analytics methodologies and embedded AI helps understand and process IoT data for meaningful business outcomes. This confluence of AI and IoT, also known as AIoT will run the entire gamut from personalization in healthcare for the connected patient to the prediction of component failures in large industrial assets. What are the benefits of augmenting traditional rules-based systems with adaptive learning systems in the world of IoT? Moving up another level, what will new innovations in computer vision technology bring to the IoT? The possibilities are limitless – enhanced industrial safety, traffic monitoring for smart city initiatives, drone inspections in agriculture that monitor health of crops, and much more. These applications will continue to improve with faster processors, more advanced machine learning algorithms and deeper integration to edge devices. In this session, participants will learn about key trends in AIoT, applications, challenges, and considerations for making ‘things’ in the internet of things more intelligent.

Speaker:

Juthika Khargharia,Ph.D, is a Principal Solutions Architect working for SAS’ Internet of Things Division specializing in machine learning, deep learning and artificial intelligence. In her role, she assists customers in defining their business problems and uses SAS advanced analytics solutions to help them reach their business goals and objectives. Juthika holds a Ph.D. in Astrophysics from the University of Colorado. Her Ph.D. research work consisted of designing observations, collecting and analyzing low signal-to-noise image data obtained from ground based telescopes to determine black hole masses in our Galaxy.

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